While the explainable ML community has apparently not yet adopted a clear taxonomy of concepts or a precise vocabulary, many authors have grappled with ideas related to interpretability and explanations. Some of these efforts include: “A Survey of Methods for Explaining Black Box Models” by [1]}, “The Mythos of Model Interpretability” by [2]}, Interpretable Machine Learning by [3]}, “Interpretable Machine Learning: Definitions,
Methods, and Applications” by [4]}, and “Challenges for Transparency” by [5]}. To decrease ambiguity herein, this section uses the review and survey corpus and practical examples to address the terms and phrases interpretable, explanation, explainable ML, interpretable models, model debugging techniques, unwanted sociological bias, and fairness techniques before proposing guidelines.
